Abstract: One or more instances of sensor data collected using an autonomous vehicle sensor suite can be labeled using corresponding instance(s) of sensor data collected using an additional sensor suite. The additional vehicle can include a second autonomous vehicle as well as a non-autonomous vehicle mounted with a removable hardware pod. In many implementations, an object in the sensor data captured using the autonomous vehicle can be labeled by mapping a label corresponding to the same object captured using the additional vehicle. In various implementations, labeled instances of sensor data can be utilized to train a machine learning model to generate one or more control signals for autonomous vehicle control.

Abstract: Sensor data collected from an autonomous vehicle can be labeled using sensor data collected from an additional vehicle. Labeled sensor data can generate targeted testing instances for a trained machine learning model, where the trained machine learning model is used in generating control signals for an autonomous vehicle. In many implementations, targeted training instances can generate an accuracy value for the trained neural network model. Additionally or alternatively, the sensor suite on the additional vehicle can include a removable hardware pod which can be mounted on a variety of vehicles.

Abstract: Sensor data collected from an autonomous vehicle data can be labeled using sensor data collected from an additional vehicle. The additional vehicle can include a non-autonomous vehicle mounted with a removable hardware pod. In many implementations, removable hardware pods can be vehicle agnostic. In many implementations, generated labels can be utilized to train a machine learning model which can generate one or more control signals for the autonomous vehicle.

Abstract: A system for monitoring a vehicle used in providing a service is disclosed. The system detects when the service is complete, and upon detecting that the service is complete, the system determines, from one or more sensors of the vehicle, that an object which should have been removed from the vehicle after completion of the transport service, remains in the vehicle. The system then automatically initiates performance of a remedial action.

Abstract: In techniques disclosed herein, machine learning models can be utilized in the control of autonomous vehicle(s), where the machine learning models are trained using automatically generated training instances. In some such implementations, a label corresponding to an object in a labeled instance of training data can be mapped to the corresponding instance of unlabeled training data. For example, an instance of sensor data can be captured using one or more sensors of a first sensor suite of a first vehicle can be labeled. The label(s) can be mapped to an instance of data captured using one or more sensors of a second sensor suite of a second vehicle.

Abstract: In techniques disclosed herein, machine learning models can be utilized in the control of autonomous vehicle(s), where the machine learning models are trained using automatically generated training instances. In some such implementations, a label corresponding to an object in a labeled instance of training data can be mapped to the corresponding instance of unlabeled training data. For example, an instance of sensor data can be captured using one or more sensors of a first sensor suite of a first vehicle can be labeled. The label(s) can be mapped to an instance of data captured using one or more sensors of a second sensor suite of a second vehicle.

Abstract: Sensor data collected from an autonomous vehicle data can be labeled using sensor data collected from an additional vehicle. The additional vehicle can include a non-autonomous vehicle mounted with a removable hardware pod. In many implementations, removable hardware pods can be vehicle agnostic. In many implementations, generated labels can be utilized to train a machine learning model which can generate one or more control signals for the autonomous vehicle.

Abstract: Sensor data collected via an autonomous vehicle can be labeled using sensor data collected via an additional vehicle, such as a non-autonomous vehicle mounted with a vehicle agnostic removable hardware pod. A training instance can include an instance of data collected by an autonomous vehicle sensor suite and one or more corresponding labels.

Abstract: Sensor data collected from an autonomous vehicle can be labeled using sensor data collected from an additional vehicle. Labeled sensor data can generate targeted testing instances for a trained machine learning model, where the trained machine learning model is used in generating control signals for an autonomous vehicle. In many implementations, targeted training instances can generate an accuracy value for the trained neural network model. Additionally or alternatively, the sensor suite on the additional vehicle can include a removable hardware pod which can be mounted on a variety of vehicles.

Abstract: Sensor data collected from an autonomous vehicle data can be labeled using sensor data collected from an additional vehicle. The additional vehicle can include a non-autonomous vehicle mounted with a removable hardware pod. In many implementations, removable hardware pods can be vehicle agnostic. In many implementations, generated labels can be utilized to train a machine learning model which can generate one or more control signals for the autonomous vehicle.

Abstract: A vehicle sensor calibration system can detect an SDV on a turntable surrounded by a plurality of fiducial targets, and rotate the turntable using a control mechanism to provide the sensor system of the SDV with a sensor view of the plurality of fiducial targets. The vehicle sensor calibration system can receive, over a communication link with the SDV, a data log corresponding to the sensor view from the sensor system of the SDV recorded as the SDV rotates on the turntable. Thereafter, the vehicle sensor calibration system can analyze the sensor data to determine a set of calibration parameters to calibrate the sensor system of the SDV.

Abstract: Sensor data collected from an autonomous vehicle can be labeled using sensor data collected from an additional vehicle. Labeled sensor data can generate targeted testing instances for a trained machine learning model, where the trained machine learning model is used in generating control signals for an autonomous vehicle. In many implementations, targeted training instances can generate an accuracy value for the trained neural network model. Additionally or alternatively, the sensor suite on the additional vehicle can include a removable hardware pod which can be mounted on a variety of vehicles.

Abstract: A vehicle sensor calibration system for self-driving vehicles can include a turntable on which a sensor system including a LIDAR sensor is positioned. A plurality of fiducial targets can be positioned around the turntable to enable calibration of the sensor system, and a control mechanism can automatically rotate the turntable when the sensor system is positioned on the turntable. The system can include one or more computing systems that receive a data log corresponding to sensor data recorded by the sensor system as the sensor system rotates on the turntable, and analyze the sensor data to determine a set of calibration parameters to calibrate the sensor system.

Abstract: A method and system for piece-picking or piece put-away within a logistics facility. The system includes a central server and at least one mobile manipulation robot. The central server is configured to communicate with the robots to send and receive piece-picking data which includes a unique identification for each piece to be picked, a location within the logistics facility of the pieces to be picked, and a route for the robot to take within the logistics facility. The robots can then autonomously navigate and position themselves within the logistics facility by recognition of landmarks by at least one of a plurality of sensors. The sensors also provide signals related to detection, identification, and location of a piece to be picked or put-away, and processors on the robots analyze the sensor information to generate movements of a unique articulated arm and end effector on the robot to pick or put-away the piece.

Abstract: A vehicle sensor calibration system for self-driving vehicles can include a turntable on which a sensor system including a LIDAR sensor is positioned. A plurality of fiducial targets can be positioned around the turntable to enable calibration of the sensor system, and a control mechanism can automatically rotate the turntable when the sensor system is positioned on the turntable. The system can include one or more computing systems that receive a data log corresponding to sensor data recorded by the sensor system as the sensor system rotates on the turntable, and analyze the sensor data to determine a set of calibration parameters to calibrate the sensor system.

Abstract: A system for monitoring a vehicle used in providing a service is disclosed. The system detects when the service is complete, and upon detecting that the service is complete, the system determines, from one or more sensors of the vehicle, that an object which should have been removed from the vehicle after completion of the transport service, remains in the vehicle. The system then automatically initiates performance of a remedial action.

Abstract: A vehicle sensor calibration system for self-driving vehicles can include a turntable on which a sensor system including a LIDAR sensor is positioned. A plurality of fiducial targets can be positioned around the turntable to enable calibration of the sensor system, and a control mechanism can automatically rotate the turntable when the sensor system is positioned on the turntable. The system can include one or more computing systems that receive a data log corresponding to sensor data recorded by the sensor system as the sensor system rotates on the turntable, and analyze the sensor data to determine a set of calibration parameters to calibrate the sensor system.

Abstract: An autonomous vehicle (AV) can include a predictive sensor configuration system that can dynamically detect reflectance anomalies that affect detectability by sensor array of the AV as the AV travels a current route. The predictive sensor configuration system can dynamically determine one or more configurations for the sensor array to attempt to positively identify the reflectance anomalies, and preemptively execute the one or more configurations for the sensor array as the AV travels the current route.

Abstract: In techniques disclosed herein, machine learning models can be utilized in the control of autonomous vehicle(s), where the machine learning models are trained using automatically generated training instances. In some such implementations, a label corresponding to an object in a labeled instance of training data can be mapped to the corresponding instance of unlabeled training data. For example, an instance of sensor data can be captured using one or more sensors of a first sensor suite of a first vehicle can be labeled. The label(s) can be mapped to an instance of data captured using one or more sensors of a second sensor suite of a second vehicle.

Abstract: One or more instances of sensor data collected using an autonomous vehicle sensor suite can be labeled using corresponding instance(s) of sensor data collected using an additional sensor suite. The additional vehicle can include a second autonomous vehicle as well as a non-autonomous vehicle mounted with a removable hardware pod. In many implementations, an object in the sensor data captured using the autonomous vehicle can be labeled by mapping a label corresponding to the same object captured using the additional vehicle. In various implementations, labeled instances of sensor data can be utilized to train a machine learning model to generate one or more control signals for autonomous vehicle control.